Feature tracking using deep learning is built upon the simple task of object detection.
Object detection is one of the most elementary problems in computer vision where one seeks to locate object instances from a set of predefined categories in images [1]}.
An object detection network takes the image as input and outputs a set of bounding boxes.
Each of these bounding boxes is typically defined by its \(x\)  and \(y\)  coordinates, box width and height, confidence that there is an object inside, and the probability of that object being from each one of the predefined classes.
